This paper deals with the problem of estimating an appropriate
hand posture to grasp an object, from 2D object’s visual cues in a
many-to-many (objects,grasp) configuration. A statistical learning protocol
implementing vector-valued regression is adopted for both classifying
the most likely grasp type and estimating the hand posture. An extensive
experimental evaluation on a publicly available dataset of visuo-motor
data reports very promising results and encourages further investigations.